This disclosure relates generally to a system and method for monitoring turbine degradation. Specifically, this disclosure relates to an automated system and method for monitoring steam turbine degradation.
Turbines suffer performance degradation over time from various sources such as solid particle erosion, deposit buildup, foreign object damage, and increased clearances due to rubs, etc. Currently, when the performance of the turbine reaches unacceptably low values, the turbine is opened to evaluate the extent and nature of the degradation and to perform corrective maintenance work to improve the condition of the turbine. Prior knowledge of the turbine health is important for planning maintenance work, scheduling outages, and ordering parts in advance of maintenance in order to minimize outage time.
Detailed steam turbine performance information is typically obtained through performance evaluations tests (PETs), which are performed either at the turbine installation or before and after an outage. In between PETs, performance is usually monitored using offline trending data from sensors in the steam turbine. This trending information is analyzed using a set of heuristic rules to decide possible causes for a given degradation. Expert engineers, who need to account for external effects that may confound the degradation in the trending data, are required to analyze the trending data.
The assessment of steam turbine performance degradation has always been important to maintaining operating margins in the power generation business, and it is becoming increasingly critical for satisfying contractual guarantees on performance, output, and availability. Current approaches to steam turbine health monitoring and diagnostics rely almost entirely on heuristic algorithms. This approach is prone to errors, due to lack of appropriate expert knowledge, incomplete sensor data, and changing system characteristics.